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It seems that Google Colab GPU’s doesn’t come with CUDA Toolkit, how can I install CUDA in Google Colab GPU’s. I am getting this error in installing mxnet in Google Colab.
Installing collected packages: mxnet Successfully installed mxnet-1.2.0
ERROR: Incomplete installation for leveraging GPUs for computations.
Please make sure you have CUDA installed and run the following line in
your terminal and try again:
pip uninstall -y mxnet && pip install mxnet-cu90==1.1.0
Adjust ‘cu90’ depending on your CUDA version (‘cu75’ and ‘cu80’ are
You can also disable GPU usage altogether by invoking turicreate.config.set_num_gpus(0).
An exception has occurred, use %tb to see the full traceback.
Cuda is not showing on your notebook because you have not enabled GPU in Colab.
The Google Colab comes with both options GPU or without GPU.
You can enable or disable GPU in runtime settings
Go to Menu > Runtime > Change runtime.
Change hardware acceleration to GPU.
To check if GPU is running or not, run the following command
After that to check if PyTorch is capable of using GPU, run the following code.
import torch torch.cuda.is_available() # Output would be True if Pytorch is using GPU otherwise it would be False.
To check if TensorFlow is capable of using GPU, run the following code.
import tensorflow as tf tf.test.gpu_device_name() # Standard output is '/device:GPU:0'
- Go here: https://developer.nvidia.com/cuda-downloads
- Select Linux -> x86_64 -> Ubuntu -> 16.04 -> deb (local)
- Copy link from the download button.
- Now you have to compose the sequence of commands. First one will be the call to wget that will download CUDA installer from the link you saved on step 3
- There will be installation instruction under “Base installer” section. Copy them as well, but remove
sudofrom all the lines.
- Preface each line with commands with
!, insert into a cell and run
- For me the command sequence was the following:
!wget https://developer.nvidia.com/compute/cuda/9.2/Prod/local_installers/cuda-repo-ubuntu1604-9-2-local_9.2.88-1_amd64 -O cuda-repo-ubuntu1604-9-2-local_9.2.88-1_amd64.deb
!dpkg -i cuda-repo-ubuntu1604-9-2-local_9.2.88-1_amd64.deb
!apt-key add /var/cuda-repo-9-2-local/7fa2af80.pub
!apt-get install cuda
- Now finally install mxnet. As cuda version I installed above is 9.2 I had to slighly change your command:
!pip install mxnet-cu92
Successfully installed graphviz-0.8.3 mxnet-cu92-1.2.0
If you switch to using GPU then CUDA will be available on your VM. Basically what you need to do is to match MXNet’s version with installed CUDA version.
Here’s what I used to install MXNet on Colab:
First check the CUDA version
!cat /usr/local/lib/python3.6/dist-packages/external/local_config_cuda/cuda/cuda/cuda_config.h |\ grep TF_CUDA_VERSION
For me it outputted
#define TF_CUDA_VERSION "8.0"
Then I installed MXNet with
!pip install mxnet-cu80
I think the easiest way here is to install mxnet-cu80. Just use the following code:
!pip install mxnet-cu80 import mxnet as mx
And you could check whether it works by:
a = mx.nd.ones((2, 3), mx.gpu()) b = a * 2 + 1 b.asnumpy()
I think colab right now just support cu80 and higher versions won’t work.
For more information, you could see the following two websites:
To run in Colab, you need CUDA 8 (mxnet 1.1.0 for cuda 9+ is broken). But Google Colab runs now 9.2. There is, however the way to uninstall 9.2, install 8.0 and then install mxnet 1.1.0 cu80.
The complete jupyter code is here : Medium